Summary of Nonconvex Federated Learning on Compact Smooth Submanifolds with Heterogeneous Data, by Jiaojiao Zhang et al.
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
by Jiaojiao Zhang, Jiang Hu, Anthony Man-Cho So, Mikael Johansson
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an algorithm for nonconvex federated learning over a compact smooth submanifold in the setting of heterogeneous client data. The proposed method leverages stochastic Riemannian gradients, manifold projection operators, local updates, and avoids client drift to improve computational efficiency and reduce communication overhead. Theoretically, it is shown that the algorithm converges sub-linearly to a neighborhood of a first-order optimal solution by exploiting the manifold structure and properties of loss functions. Numerical experiments demonstrate its effectiveness compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in machine learning called federated learning on curved spaces. Imagine you have many devices that need to work together to learn something new, but they can’t share all their data because it’s private. The algorithm helps these devices learn from each other more efficiently and accurately by using special mathematical tools like Riemannian gradients and manifold projections. This could be used in lots of real-world applications where you have many devices or people that need to work together. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
